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buckley-smith igarss2011.pptx
1. National Défense Defence national Comparing RADARSAT 2 and TerraSAR-X Quad-Pol SAR Imagery of Grasslands Joseph R. Buckley1 and Anne M. Smith21Royal Military College of Canada, Kingston, ON, Canada2Agriculture and Agri-Food Canada, Lethbridge, AB, Canada
2. Outline Setting The Scene Experimental Design and Procedures Some Results Conclusions and Continuing Work
3. The Big Picture “Developing Earth Observation Tools to Measure the Current and Future Spatial Extent and Productivity in Grasslands of Western Canada” A Multi-Year, Multi-Agency Experiment Canadian Space Agency Funded
13. Agriculture, oil & gas, urbanization, over grazing, invasive speciesEnhancing environmental performance of the Canadian agricultural system, enhancing economic benefits for all stakeholders
14. Native grassland Photographs courtesy of: G. Bourgeois, M. Didkowsky R. Bourchier, J. Nicholsen, G. Larson, C. Kloppenburg.
15. Management decisions (stocking rates, grazing rotation) Grazing Associations, Ranchers Land management (e.g. weed enforcement, pest control, soil conservation) Land manager Image supply Mapping products Land management ATIC, CSA, TerraVista Earth Imaging Industry Municipal Health assessment Wildlife habitat Landscape cumulative effects, Land Use Framework, BMP ASRD, AFSC Grassland NGO Provincial Health assessment Wildlife habitat ACA University Federal Risk assessment/Policy Species at risk Pasture management Weed biocontrol Program evaluation (BMP, NCGAVS, NAHARP)AAFC (Research Branch/AESB, EC (DND, INAC, CFIA) Airborne hyperspectral Calibration Ecosystem modeling Weed invasion ULeth, RMC, UND, UVic Grasslands Project Team
16. Objectives To develop multispectral, hyperspectral and radar earth observation tools to address mapping and quantification of: the spatial extent and fragmentation of grasslands, the net primary productivity of grasslands, the invasive plant species, leafy spurge, on grasslands.
17. Progress to Date We are in the third year of this two year project Test sites Throughout Southern Alberta Ground data Surveys, crop insurance and irrigation databases (2009, 2010, 2011) Remote sensing data Optical Landsat-5 TM (1999, 2005, 2009) SPOT4 (2009) Radarsat-2 (2009, 2010, 2011) Airborne Hyperspectral (2009, 2010) Lidar (2009, 2010) 8
20. The POLSAR Comparison Experiment DLR graciously accepted our request to participate in the spring 2010 DRA campaign, and acquired two three image quad-polstripmap sets over two of our 2009-2010 test areas. Three RADARSAT-2 fine mode quad pol images were acquired in as close to matching incidence angles and dates as could be arranged. In mid-December 2010 we received the 6 SSC images from DLR Both RADARSAT-2 and TerraSAR-X images have been ingested, speckle filtered (Lee Sigma) and decomposed (Yamaguchi4) with PolSARpro, georeferenced with ENVI and the tiepoints provided with the imagery, then fine co-registered by the usual cross-correlation method. Preliminary comparative analysis has been done on the imagery.
21. General Conditions Data Acquisition: 17 April to 11 May 2010 On the southern Alberta prairies Snow had melted Lakes were no longer ice covered No rain No significant natural growth Preparing and planting crops had not yet started Natural rangelands were covered in senescent vegetation Cultivated lands were either stubble, fallow or plowed
22. Observation Programme RADARSAT-2 April 17 FQ8 26.9 – 28.7 RADARSAT-2 April 27 FQ2 19.8 – 21.8 RADARSAT-2 May 1 FQ17 36.4 – 38.0 TerraSAR-X April 18, 29, May 10 Stripmap Far 30.8 – 32.3 TerraSAR-X April 12, 23, May 4 Stripmap Near 19.9 – 21.7
23. Yamaguchi 4 Decomposition SurfaceVolumeDouble All scaled -30 0dB TerraSAR-X April 18Stripmap Far 30.8 – 32.3 RADARSAT-2 April 17 FQ8 26.9 – 28.7
24. RADARSAT-2 : April 27 19.8 – 21.8 TerraSAR-X: April 23 19.9 – 21.7 RADARSAT-2 V S D V S D Yamaguchi 4 Decomposition Cyan: TSX Red:R2 Surface scattering TerraSAR-X
25. RADARSAT-2 : April 17 26.9 – 28.7 TerraSAR-X: April 18 30.8 – 32.3 RADARSAT-2 V S D V D Yamaguchi 4 Decomposition Cyan: TSX Red:R2 Surface scattering S TerraSAR-X
26. RADARSAT-2 : May 1 36.4 – 38.0 TerraSAR-X: April 29 30.8 – 32.3 RADARSAT-2 V S D V S D Yamaguchi 4 Decomposition Cyan: TSX Red:R2 Surface scattering TerraSAR-X
27. Scatter Plots: April 17-18, Rangeland ROI Surface Volume Dihedral X axis: TerraSAR-X Y axis: RADARSAT-2
28. Observations Both RADARSAT-2 and TerraSAR-X show the same general structure TerraSAR-X is always brighter than RADARSAT-2 There is no correlation at the pixel level between images from the two sensors
45. April 18 May 10 April 29 FQ8 April 17 FQ17 May 1
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47. Conclusions Qualitatively, RADARSAT-2 and TerraSAR-X produce similar imagery. TerraSAR-X imagery is visibly noisier. Very high entropies for TerraSAR-X imagery indicate little statistically significant polarimetric information These results are specific to the environmental conditions Is the environment of the prairies in early spring too subtle for TerraSAR-X quad-pol? A Cautionary note!
